DSP technology can perform spatial multi-omics analysis of proteome and transcriptome simultaneously. Its core principle is to use DNA oligos coupled to antibodies or RNA probes to label targets, release oligos through laser cutting and quantify.

• Protein analysis: supports up to 96 protein targets, and spatial positioning is achieved through immunofluorescence labeling.
• RNA analysis: can detect more than 1,000 RNA targets (need to be combined with second-generation sequencing).
• Spatial resolution: The minimum laser accuracy is 10 microns, close to the single-cell level (but needs to be combined with morphological markers for fine partitioning).

The core advantages of DSP are:

• Spatial retention: direct in situ analysis of tissue sections to avoid the loss of spatial information caused by single-cell dissociation.
• Multi-omics integration: proteins and RNA can be detected simultaneously in the same sample, supporting the study of complex interaction mechanisms.
• Sample non-destructive: Laser cutting only collects oligos, and tissues can be reused for other experiments (such as IHC verification).
• Flexible partitioning: Allows manual or automatic selection of regions of interest (ROI) of any shape, suitable for tissues with high heterogeneity (such as tumors).

DSP is compatible with conventional clinical sample types:

• Tissue types: including formalin-fixed paraffin-embedded (FFPE) and frozen tissue sections.
• Pretreatment requirements: Antibody or RNA probe staining is required, and ROI selection is assisted by fluorescent labeling.
• Quality control: Immunohistochemistry (such as GFAP, Iba-1) is required to verify the accuracy of target positioning.

The data analysis process of DSP includes:

• Image association: determine the spatial position of ROI through fluorescent labeled images;
• Target quantification: the collected Oligo is quantified by the nCounter system;
• Data integration: combine expression data with tissue morphology and cell type markers (such as neuronal Map2, microglial CD45) to reveal spatial specific molecular characteristics.

For example, in the study of ischemic stroke, DSP revealed the spatial gradient changes of reduced neuronal proteins and increased inflammatory markers in the core area.

Typical application scenarios:

• Disease mechanism research: such as the spatial dynamics of neuroinflammation after stroke (ischemic core vs. surrounding tissue), multi-organ molecular atlas of COVID-19 lung injury.
• Tumor microenvironment: analyze the interaction between tumor cells and immune cells in lung adenocarcinoma (combined with single-cell sequencing).
• Therapeutic target discovery: screen intervention targets for neurodegenerative diseases (such as tau protein phosphorylation) through spatial proteomics.